Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graph separators, with applications
Graph separators, with applications
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Sparse graphical models for exploring gene expression data
Journal of Multivariate Analysis
A Recursive Method for Structural Learning of Directed Acyclic Graphs
The Journal of Machine Learning Research
An improved shrinkage estimator to infer regulatory networks with Gaussian graphical models
Proceedings of the 2009 ACM symposium on Applied Computing
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Efficient Markov network structure discovery using independence tests
Journal of Artificial Intelligence Research
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning Gaussian graphical models of gene networks with false discovery rate control
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
Reverse engineering of gene regulatory networks from biological data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Learning undirected graphical models from multiple datasets with the generalized non-rejection rate
International Journal of Approximate Reasoning
Identifying significant edges in graphical models of molecular networks
Artificial Intelligence in Medicine
A comparative study of covariance selection models for the inference of gene regulatory networks
Journal of Biomedical Informatics
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Learning of large-scale networks of interactions from microarray data is an important and challenging problem in bioinformatics. A widely used approach is to assume that the available data constitute a random sample from a multivariate distribution belonging to a Gaussian graphical model. As a consequence, the prime objects of inference are full-order partial correlations which are partial correlations between two variables given the remaining ones. In the context of microarray data the number of variables exceed the sample size and this precludes the application of traditional structure learning procedures because a sampling version of full-order partial correlations does not exist. In this paper we consider limited-order partial correlations, these are partial correlations computed on marginal distributions of manageable size, and provide a set of rules that allow one to assess the usefulness of these quantities to derive the independence structure of the underlying Gaussian graphical model. Furthermore, we introduce a novel structure learning procedure based on a quantity, obtained from limited-order partial correlations, that we call the non-rejection rate. The applicability and usefulness of the procedure are demonstrated by both simulated and real data.